Two-dimensional(2D)semiconductors isoelectronic to phosphorene have been drawing much attention recently due to their promising applications for next-generation(opt)electronics.This family of 2D materials contains mor...Two-dimensional(2D)semiconductors isoelectronic to phosphorene have been drawing much attention recently due to their promising applications for next-generation(opt)electronics.This family of 2D materials contains more than 400members,including(a)elemental group-V materials,(b)binary III–VII and IV–VI compounds,(c)ternary III–VI–VII and IV–V–VII compounds,making materials design with targeted functionality unprecedentedly rich and extremely challenging.To shed light on rational functionality design with this family of materials,we systemically explore their fundamental band gaps and alignments using hybrid density functional theory(DFT)in combination with machine learning.First,calculations are performed using both the Perdew–Burke–Ernzerhof exchange–correlation functional within the generalgradient-density approximation(GGA-PBE)and Heyd–Scuseria–Ernzerhof hybrid functional(HSE)as a reference.We find this family of materials share similar crystalline structures,but possess largely distributed band-gap values ranging approximately from 0 eV to 8 eV.Then,we apply machine learning methods,including linear regression(LR),random forest regression(RFR),and support vector machine regression(SVR),to build models for the prediction of electronic properties.Among these models,SVR is found to have the best performance,yielding the root mean square error(RMSE)less than 0.15 eV for the predicted band gaps,valence-band maximums(VBMs),and conduction-band minimums(CBMs)when both PBE results and elemental information are used as features.Thus,we demonstrate that the machine learning models are universally suitable for screening 2D isoelectronic systems with targeted functionality,and especially valuable for the design of alloys and heterogeneous systems.展开更多
Two-dimensional (2D) crystals are known to have no bulk but only surfaces and edges, thus leading to unprecedented properties thanks to the quantum confinements. For half a century, the compression of z-dimension has ...Two-dimensional (2D) crystals are known to have no bulk but only surfaces and edges, thus leading to unprecedented properties thanks to the quantum confinements. For half a century, the compression of z-dimension has been attempted through ultra-thin films by such as molecular beam epitaxy. However, the revisiting of thin films becomes popular again, in another fashion of the isolation of freestanding 2D layers out of van der Waals (vdW) bulk compounds. To date, nearly two decades after the nativity of the great graphene venture, researchers are still fascinated about flattening, into the atomic limit, all kinds of crystals, whether or not they are vdW. In this introductive review, we will summarize some recent experimental progresses on 2D electronic systems, and briefly discuss their revolutionizing capabilities for the implementation of future nanostructures and nanoelectronics.展开更多
基金Project supported by the National Key R&D Program of China(Grant No.2017YFA0206301)。
文摘Two-dimensional(2D)semiconductors isoelectronic to phosphorene have been drawing much attention recently due to their promising applications for next-generation(opt)electronics.This family of 2D materials contains more than 400members,including(a)elemental group-V materials,(b)binary III–VII and IV–VI compounds,(c)ternary III–VI–VII and IV–V–VII compounds,making materials design with targeted functionality unprecedentedly rich and extremely challenging.To shed light on rational functionality design with this family of materials,we systemically explore their fundamental band gaps and alignments using hybrid density functional theory(DFT)in combination with machine learning.First,calculations are performed using both the Perdew–Burke–Ernzerhof exchange–correlation functional within the generalgradient-density approximation(GGA-PBE)and Heyd–Scuseria–Ernzerhof hybrid functional(HSE)as a reference.We find this family of materials share similar crystalline structures,but possess largely distributed band-gap values ranging approximately from 0 eV to 8 eV.Then,we apply machine learning methods,including linear regression(LR),random forest regression(RFR),and support vector machine regression(SVR),to build models for the prediction of electronic properties.Among these models,SVR is found to have the best performance,yielding the root mean square error(RMSE)less than 0.15 eV for the predicted band gaps,valence-band maximums(VBMs),and conduction-band minimums(CBMs)when both PBE results and elemental information are used as features.Thus,we demonstrate that the machine learning models are universally suitable for screening 2D isoelectronic systems with targeted functionality,and especially valuable for the design of alloys and heterogeneous systems.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11974357 and U1932151)the State Key Research Development Program of China(Grant No.2019YFA0307800)+1 种基金the Program of State Key Laboratory of Quantum Optics and Quantum Optics Devices,China(Grant No.KF201816)the Major Program of Aerospace Advanced Manufacturing Technology Research Foundation NSFC and CASC,China(Grant No.U1537204).
文摘Two-dimensional (2D) crystals are known to have no bulk but only surfaces and edges, thus leading to unprecedented properties thanks to the quantum confinements. For half a century, the compression of z-dimension has been attempted through ultra-thin films by such as molecular beam epitaxy. However, the revisiting of thin films becomes popular again, in another fashion of the isolation of freestanding 2D layers out of van der Waals (vdW) bulk compounds. To date, nearly two decades after the nativity of the great graphene venture, researchers are still fascinated about flattening, into the atomic limit, all kinds of crystals, whether or not they are vdW. In this introductive review, we will summarize some recent experimental progresses on 2D electronic systems, and briefly discuss their revolutionizing capabilities for the implementation of future nanostructures and nanoelectronics.
